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Record W2099438588 · doi:10.1109/tpel.2008.2009055

Minimize Low-Order Harmonics in Low-Switching-Frequency Space-Vector-Modulated Current Source Converters With Minimum Harmonic Tracking Technique

2009· article· en· W2099438588 on OpenAlexaff
M. F. Naguib, Luiz A. C. Lopes

Bibliographic record

VenueIEEE Transactions on Power Electronics · 2009
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsConcordia University
Fundersnot available
KeywordsHarmonicsConvertersSpace vector modulationHarmonicTopology (electrical circuits)Reduction (mathematics)State (computer science)AlgorithmComputer scienceElectronic engineeringElectrical engineeringMathematicsPhysicsEngineeringVoltagePulse-width modulation

Abstract

fetched live from OpenAlex

Gate turnoffs (GTOs) are usually used in high-power current source converters (CSCs), i.e., rectifiers and inverters. Space vector modulation (SVM) technique for CSC is established by dividing ac-side line current cycle into six sectors. Each sector is divided into a certain number of SV cycles. SV cycle is divided into three states: two active and one zero state. For low switching frequency as required by GTOs, the SVM technique generates fifth and seventh harmonics (HD <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5-7</sub> ) in the CSC ac-side current. Minimal reduction in HD <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5-7</sub> was achieved with certain states sequence inside the SV cycle. Moderate reduction in HD <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5-7</sub> was obtained by calculating states on -times at once in the middle of each SV cycle. In this paper, larger reduction in HD <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5-7</sub> at CSC ac-side current is achieved by new techniques for calculating states on-times. First, two straightforward techniques are proposed. One calculates states on-times from SVM equations in the middle of each state on-time. The other calculates all states on -times when the state changes from one active state to the other. Both techniques are effective in reducing HD <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5-7</sub> . Then, minimum harmonics tracking (MHT) technique for calculating states on-times in SVM CSC is proposed. Tracking technique adjusts states on-times once per four ac-side line current cycles to give the least HD <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5-7</sub> . In CSC with a large overlap period, power factor affects HD <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5-7</sub> , so two-variables MHT technique for both active states on-times inside SV cycle is proposed to give the least HD <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5-7</sub> . Also, a variable perturbation tracking technique is proposed to reduce transient time with unperturbed steady-state operation. Finally, experimental investigations and obstacles are introduced.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.854
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.213
Teacher spread0.205 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations20
Published2009
Admission routes1
Has abstractyes

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